KEYWORDS: Modulation transfer functions, Pupil aberrations, Wavefront aberrations, Wavefronts, Deconvolution, Point spread functions, Imaging systems, Signal to noise ratio, Image deconvolution, Binary data
The performance of an imaging system is limited by optical aberrations, which cause blurriness in the resulting image. Digital correction techniques, such as deconvolution, have limited ability to correct the blur, since some spatial frequencies in the scene are not measured adequately due to the aberrations (‘zeros’ of the system transfer function). Our work proves that the addition of a random mask to an imaging system removes its dependence on aberrations, resulting in no systematic zeros in the transfer function and consequently less sensitivity to noise during deconvolution. In simulation, we show that this strategy improves image quality over a range of aberration types, aberration strengths, and signal-to-noise ratios.
Multiphoton microscopy (MPM) provides high-resolution imaging of deep tissue structures while allowing for the visualization of non-labeled biological samples. However, photon generation efficiency of intrinsic biomarkers is low and this, coupled with inherent detection inaccuracies in the photoelectric sensors, leads to an introduction of noise in acquired images. Higher dwelling times can reduce noise but increase the likelihood of photobleaching. To combat this, deep learning methods are being increasingly employed to denoise MPM images, allowing for a more efficient and less invasive process. However, machine learning models can hallucinate information, which is unacceptable for critical scientific microscopy applications. Uncertainty quantification, which has been demonstrated for image-to-image regression tasks, can provide confidence bounds for machine learning-based image reconstruction tasks, adding confidence to predictions. In this work, we discuss incorporating uncertainty quantification into an optimized denoising model to guide adaptive multiphoton microscopy image acquisition. We demonstrate that our method is capable of maintaining fine features in the denoised image, while outperforming other denoising methods by adaptively selecting to reimage the most uncertain pixels in a human endometrium tissue sample.
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